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Arrays with large number of microphones can be very effective on audio processing tasks, like denoising, acoustic echo removal, etc. New microphone technologies enable creating large arrays with very low cost per component, but the system can still be very expensive due to costs of transmitting all signals to a single processor, and the computational resources to process the large amount of data. We show how a massively-parallel signal processing approach can solve the cost issues, when applied to the problem of sound source localization. We consider the case where each microphone is coupled to simple processing circuitry, which have full-bandwidth access to data from a few other microphones, while only shared power and low-bandwidth connections are provided between each microphone and a central processor. We discuss implementation issues, and show experimental results obtained in simulations and in microphone array measurements.